International Journal of Science, Engineering and TechnologyAuthors: Asst. Professor C.P.Lachake, Tushar Yadav, Siddhisri Kannekanti, Shuti Raut, Pranjal Narsale
Abstract: Agricultural productivity plays a vital role in the economic stability of many developing countries. However, the rise of plant diseases causes significant losses to farmers and agricultural industries every year. Early detection and accurate identification of plant diseases are essential to prevent these losses. This paper proposes a Plant Disease Detector Application, a mobile-based system designed using the Flutter framework integrated with a Convolutional Neural Network (CNN) model built using TensorFlow and Keras. The app enables users to capture images of infected plant leaves and instantly identify the disease along with suggested preventive measures. The proposed model is trained on the PlantVillage dataset, achieving a testing accuracy of over 97%. The integration of TensorFlow Lite allows the model to run efficiently on smartphones, even without internet connectivity. This application demonstrates a scalable, cost-effective solution for farmers, researchers, and agricultural institutions to promote sustainable farming through technology-driven management
